1 citations · 1 across the 12 of their papers we have counts for
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Active Inference as a Convex Markov Decision Process
Nikola Milosevic, Nicolás Hinrichs, Nico Scherf
Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principl…
Predictive Statistics Shape Emergent World Representations of Grid Walkers
Sasha Brenner, Thomas R. Knösche, Nico Scherf
Next-token predictors often appear to develop internal representations of the latent world and its rules. The probabilistic nature of these models suggests a deep connection betwee…
Stochastic Decision Horizons for Constrained Reinforcement Learning
Nikola Milosevic, Leonard Franz, Daniel Haeufle +3
We propose stochastic decision horizons (SDH), a theoretically grounded framework for solving constrained RL problems with every-step constraint satisfaction, a desirable property…
Predicting Microbial Interactions Using Graph Neural Networks
Elham Gholamzadeh, Kajal Singla, Nico Scherf
Predicting interspecies interactions is a key challenge in microbial ecology, as these interactions are critical to determining the structure and activity of microbial communities.…
The Geometry of Nonlinear Reinforcement Learning
Nikola Milosevic, Nico Scherf
Reward maximization, safe exploration, and intrinsic motivation are often studied as separate objectives in reinforcement learning (RL). We present a unified geometric framework, t…
Central Path Proximal Policy Optimization
Nikola Milosevic, Johannes Müller, Nico Scherf
In constrained Markov decision processes, enforcing constraints during training is often thought of as decreasing the final return. Recently, it was shown that constraints can be i…